def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
elif dataset.lower() == 'visualgenome':
train_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets',
'visualgenome'),
splits='detections_train',
use_crowd=False)
val_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets',
'visualgenome'),
splits='detections_val',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def metrics(self):
_, _, H, W = self.ishape
metric = COCODetectionMetric(self.val_dataset,
'_eval',
cleanup=True,
data_shape=(H, W))
metric.reset()
return metric
def metrics(self):
""" Customized metrics method introduction.
COCODetectionMetric is used which is the detection metric for COCO bbox task.
"""
_, _, H, W = self.ishape
metric = COCODetectionMetric(
self.val_dataset, '_eval', cleanup=True, data_shape=(H, W))
metric.reset()
return metric
def get_coco(opt, coco_path="/export/guanghan/coco"):
"""Get coco dataset."""
val_dataset = CenterCOCODataset(opt, split = 'val') # custom dataset
eval_metric = COCODetectionMetric(val_dataset,
save_prefix = '_eval',
data_shape=(opt.input_res, opt.input_res))
return val_dataset, eval_metric
def get_coco_validation_metric(val_data, consts):
val_metric = COCODetectionMetric(val_data,
consts.VAL_METRIC_F_NAME,
cleanup=True,
data_shape=(consts.IN_SIZE,
consts.IN_SIZE))
return val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if 0:
train_dataset = gdata.VOCDetection(root='E:/dataset/VOCdevkit',
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root='E:/dataset/VOCdevkit',
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
else:
voc_root = 'G:/MSDataset/' #layout same with VOC07
train_dataset = gdata.MSDetection(root=voc_root,
splits=[(2007, 'trainval')])
val_dataset = gdata.MSDetection(root=voc_root,
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(root=args.dataset_root,
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.dataset_root,
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017')
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
elif dataset.lower() == 'tt100k':
train_dataset = gdata.TT100KDetection(root=args.dataset_root,
splits='train')
val_dataset = None
val_metric = None
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
pass
# train_dataset = gdata.VOCDetection(
# splits=[(2007, 'trainval'), (2012, 'trainval')])
# val_dataset = gdata.VOCDetection(
# splits=[(2007, 'test')])
# val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(
root='/home/xcq/PycharmProjects/datasets/coco/',
splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(
root='/home/xcq/PycharmProjects/datasets/coco/',
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = VOCLike(
root='C:/Users/DELL/Desktop/traindata/VOCtemplate',
splits=((2018, 'train'), ))
val_dataset = VOCLike(
root='C:/Users/DELL/Desktop/traindata/VOCtemplate',
splits=((2018, 'val'), ))
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
# load training and validation images
if dataset.lower == 'voc':
train_dataset = gdata.VOCDetection(root=args.dataset_root + "/voc",
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.dataset_root + "/voc",
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
# if class_names is provided, will print out AP for each class
elif dataset.lower == 'coco':
train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# will print out AP for each class
if args.val_interval == 1:
# args.val_interval: 进行验证集测试的循环间隔
# 如果进行测试很慢的话， 需要将该值改大一些，以加快训练
args.val_interval = 10
else:
raise NotImplementedError(
"dataset: {} not implemented".format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = VOCLike(
root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/",
splits=[(2007, 'train')])
val_dataset = VOCLike(
root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/",
splits=[(2007, 'validation')])
print(train_dataset.classes)
print(val_dataset.classes)
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = VOCLike(root='/opt/ml/input/data/training',
splits=(('VOCTrain', 'train'), ))
val_dataset = VOCLike(root='/opt/ml/input/data/training',
splits=(('VOCValidate', 'val'), ))
#train_dataset = VOCLike(root='VOC-PlayingCards', splits=(('VOC2019', 'train'),))
#val_dataset = VOCLike(root='VOC-PlayingCards', splits=(('VOC2018', 'val'),))
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
for c in val_metric.class_names:
print("Class: {}".format(c))
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
train_dataset = petVOC(splits=[(2019, 'train_val')])
val_dataset = train_dataset
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
#train_dataset = VOCLike(root='/opt/ml/input/data/training', splits=((2019, 'train'),))
#val_dataset = VOCLike(root='/opt/ml/input/data/training', splits=((2018, 'val'),))
train_dataset = VOCLike(
root='~/code/gluoncv-yolo-playing_cards/VOCTemplate',
splits=((2019, 'train'), ))
val_dataset = VOCLike(
root='~/code/gluoncv-yolo-playing_cards/VOCTemplate',
splits=((2018, 'val'), ))
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
if args.mixup:
from gluoncv.data import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
root='/home/users/chenxin.lu/VOCdevkit/VOCdevkit',
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(
root=args.dataset_root + "/coco/stuff_annotations_trainval2017",
splits='stuff_train2017')
val_dataset = gdata.COCODetection(
root=args.dataset_root + "/coco/stuff_annotations_trainval2017",
splits='stuff_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape),
post_affine=get_post_transform)
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR,
splits=('person_train2014',
'person_train2017'),
use_crowd=False)
val_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR,
splits='person_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
if args.mixup:
from gluoncv.data import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(
splits=[(2007, 'test')])
#print(val_dataset.classes)
#('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
elif dataset.lower() == 'pedestrian':
lst_dataset = LstDetection('train_val.lst',root=os.path.expanduser('.'))
print(len(lst_dataset))
first_img = lst_dataset[0][0]
print(first_img.shape)
print(lst_dataset[0][1])
train_dataset = LstDetection('train.lst',root=os.path.expanduser('.'))
val_dataset = LstDetection('val.lst',root=os.path.expanduser('.'))
classs = ('pedestrian',)
val_metric = VOC07MApMetric(iou_thresh=0.5,class_names=classs)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dali_dataset(dataset_name, devices, args):
if dataset_name.lower() == "coco":
# training
expanded_file_root = os.path.expanduser(args.dataset_root)
coco_root = os.path.join(expanded_file_root,
'coco',
'train2017')
coco_annotations = os.path.join(expanded_file_root,
'coco',
'annotations',
'instances_train2017.json')
if args.horovod:
train_dataset = [gdata.COCODetectionDALI(num_shards=hvd.size(), shard_id=hvd.rank(), file_root=coco_root,
annotations_file=coco_annotations, device_id=hvd.local_rank())]
else:
train_dataset = [gdata.COCODetectionDALI(num_shards= len(devices), shard_id=i, file_root=coco_root,
annotations_file=coco_annotations, device_id=i) for i, _ in enumerate(devices)]
# validation
if (not args.horovod or hvd.rank() == 0):
val_dataset = gdata.COCODetection(root=os.path.join(args.dataset_root + '/coco'),
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(
val_dataset, args.save_prefix + '_eval', cleanup=True,
data_shape=(args.data_shape, args.data_shape))
else:
val_dataset = None
val_metric = None
else:
raise NotImplementedError('Dataset: {} not implemented with DALI.'.format(dataset_name))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco",
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
elif dataset.lower() == 'nzrc':
# The classes for the dataset need to be reset after net is loaded to prevent a classes mismatch errors when loading net.
gdata.COCODetection.CLASSES = classes
print("train_efficirntdet.py-50 get_dataset CLASSES=",
gdata.COCODetection.CLASSES)
train_dataset = gdata.COCODetection(root=args.dataset_root +
"/NZRC/ML4DR_v2",
splits='coco_export2_train')
val_dataset = gdata.COCODetection(root=args.dataset_root +
"/NZRC/ML4DR_v2",
splits='coco_export2_val',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(args):
train_dataset = gdata.COCODetection(
root=args.datasetloc, splits="instances_train2017", use_crowd=False, skip_empty=True
)
val_dataset = gdata.COCODetection(
root=args.datasetloc, splits="instances_val2017", skip_empty=False
)
val_metric = COCODetectionMetric(val_dataset, args.save_prefix + "_eval")
return train_dataset, val_dataset, val_metric
def get_dataset(args):
train_dataset = COCODetection(args.train, 'train', args.images, 'labels')
val_dataset = COCODetection(args.test, 'test', args.images, 'labels')
val_metric = COCODetectionMetric(
val_dataset, args.save_prefix + '_eval', cleanup=True,
data_shape=(args.data_shape, args.data_shape)
)
args.num_samples = len(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
val_dataset = gdata.VOCDetection(
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
cleanup=not args.save_json)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric
def get_dataset(dataset, data_shape):
if dataset.lower() == 'voc':
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
#val_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR, splits='person_train2017', skip_empty=False)
#val_dataset = gdata.COCODetection(root=COCO_ROOT_DIR, splits='instances_val2017', skip_empty=False)
val_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR, splits='person_val2017', skip_empty=False)
val_metric = COCODetectionMetric(
val_dataset, args.save_prefix + '_eval', cleanup=True,
data_shape=(data_shape, data_shape))
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'coco':
from lib.dataset.coco import COCODataset
val_dataset = COCODataset(root=args.val_data_root,
annFile=args.val_ann_file,
use_crowd=False)
# train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
# val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return None, val_dataset, val_metric
def get_dataset(dataset, args):
width, height = args.data_shape, args.data_shape
if dataset.lower() == 'coco':
val_dataset = COCODetection(root='./data/coco',
splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=not args.save_json,
data_shape=(height, width),
score_thresh=args.score_thresh)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
# train_dataset = gdata.VOCDetection(
# splits=[(2007, 'trainval'), (2012, 'trainval')])
# val_dataset = gdata.VOCDetection(
# splits=[(2007, 'test')])
# val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
root = '/home/wangqiushi/pycode/datasets/DR4/'
image_root = os.path.join(root, 'region_pics')
#image_root = '/'
train_label_file = os.path.join(root, 'train.txt')
val_label_file = os.path.join(root, 'valid.txt')
classes = ('40', '50')
map_file = os.path.join(root, 'DR2_map.txt')
train_dataset = DetectionDataset(image_root,
train_label_file,
classes,
map_file=map_file)
val_dataset = DetectionDataset(image_root,
val_label_file,
classes,
map_file=map_file,
shuffle=False)
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017')
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
# coco validation is slow, consider increase the validation interval
if args.val_interval == 1:
args.val_interval = 10
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_eval_metric(args, CLASSES=None, IOU_THRESH=0.5):
if args.pre_trained_weights == 'VOC':
eval_metric = VOC07MApMetric(iou_thresh=IOU_THRESH,
class_names=CLASSES)
elif args.pre_trained_weights == 'COCO':
eval_metric = COCODetectionMetric(val_dataset,
args.save_prefix + '_eval',
cleanup=True,
data_shape=(args.data_shape,
args.data_shape))
elif args.pre_trained_weights == 'NONE':
eval_metric = VOC07MApMetric(iou_thresh=IOU_THRESH,
class_names=CLASSES)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return eval_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.COCODetection(splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(splits='instances_val2017',
skip_empty=False)
val_metric = COCODetectionMetric(val_dataset,
os.path.join(args.logdir, 'eval'),
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if cfg.TRAIN.MODE_MIXUP:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(root=args.data_path,
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.data_path,
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
#train_dataset = gdata.COCODetection(splits='instances_train2014', use_crowd=False)
train_dataset = gdata.COCODetection(root=args.data_path, splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.data_path, splits='instances_val2017', skip_empty=False)
val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
elif dataset.lower() == 'rec':
train_dataset = gdata.RecordFileDetection(os.path.join(args.data_path, 'pikachu_train.rec'))
val_dataset = gdata.RecordFileDetection(os.path.join(args.data_path, 'pikachu_train.rec'))
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=rec_classes)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
